import time
import numpy as np
from PIL import Image
import torch


# ---------------------------------------------------------#
#   将图像转换成RGB图像，防止灰度图在预测时报错。
#   代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
# ---------------------------------------------------------#
def cvtColor(image):
    if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
        return image
    else:
        image = image.convert("RGB")
        return image


# ---------------------------------------------------#
#   对输入图像进行resize
# ---------------------------------------------------#
def resize_image(image, size):
    iw, ih = image.size
    w, h = size

    scale = min(w / iw, h / ih)
    nw = int(iw * scale)
    nh = int(ih * scale)

    image = image.resize((nw, nh), Image.BICUBIC)
    new_image = Image.new("RGB", size, (128, 128, 128))
    new_image.paste(image, ((w - nw) // 2, (h - nh) // 2))

    return new_image, nw, nh


# ---------------------------------------------------#
#   获得学习率
# ---------------------------------------------------#
def get_lr(optimizer):
    for param_group in optimizer.param_groups:
        return param_group["lr"]


def preprocess_input(image):
    image /= 255.0
    return image


def show_config(**kwargs):
    print("Configurations:")
    print("-" * 70)
    print("|%25s | %40s|" % ("keys", "values"))
    print("-" * 70)
    for key, value in kwargs.items():
        print("|%25s | %40s|" % (str(key), str(value)))
    print("-" * 70)


# -----------------------------------------------------#
# 等待当前设备上所有流中的所有核心完成 返回当前的时间戳
# -----------------------------------------------------#
def time_synchronized():
    torch.cuda.synchronize() if torch.cuda.is_available() else None
    return time.time()
